The present invention relates to ambulatory monitoring and, more particularly, to physiological signal quality classification methods and systems designed to improve ambulatory monitoring.
Ambulatory monitoring of the physiological state of people who suffer from chronic diseases is an important aspect of chronic disease management. By way of example, ambulatory monitoring is in widespread use managing chronic diseases such as asthma and in elder care.
Ambulatory monitoring is often performed using wearable devices that acquire and analyze physiological signals, such as heart and lung sounds, as people go about their daily lives. These signals are not always reliable. For example, a signal may be too noisy when a person speaks, or is in motion, or is in an environment with high background noise. Moreover, a signal may be too weak when a person does not place a sensor of the device at the proper body location or when an air chamber of the sensor is not fully sealed. When a signal is too noisy or too weak, confidence in physiological data extracted from the signal, such as the patient's heart rate, may be very low.
Physiological data extracted from an unreliable physiological signal can have serious adverse consequences on patient health. For example, such physiological data can lead a patient or his or her clinician to improperly interpret the patient's physiological state and cause the patient to undergo treatment that is not medically indicated or forego treatment that is medically indicated.